It has been contended that violence is prevalent in the workplace, and there has been increasing research interest into its potential effects. Human interactions at workplaces are apparent. However, the interactions among humans may have positive or negative dimensions. Usually, the positive or negative interactions between workers lead to different outcomes. Sometimes, they lead to a productive working environment; however, in some cases, they lead to toxicity among workers. In this study, we investigate the impact of workplace violence (WV) on innovative work behavior (IWB). Specifically, it examines the impact of the three dimensions of WV, namely, harassment, mobbing, and sabotage. Moreover, employees’ wellbeing mediates the relationship between WV (harassment, mobbing, and sabotage) and IWB. A questionnaire survey approach was used in this study. The target population were the workers of SMEs entrepreneurs located in Guangdong Province (China). The results confirm that, in the direct relationship, WV (harassment, mobbing, and sabotage) has a negative relationship with innovative IWB. Moreover, results also confirm that employee wellbeing is mediated between WV (harassment, mobbing, and sabotage) and IWB. Therefore, the empirical results of this paper identify that workplace violence reduces employees’ innovative work behavior by reducing their subjective and eudemonic wellbeing, which further broadens the perspective of IWB’s motivation analysis. Practical implications for small and medium enterprise organizations have also been discussed in this paper.

Global climate change’s pervasive impacts extend beyond the environment, significantly affecting mental health across diverse regions. This study offers a comprehensive multi-regional analysis spanning Asia, Africa, Oceania, Europe, and the Americas, addressing three critical gaps in existing research: (i) the necessity of a global scope given climate change’s widespread impact, (ii) the under-researched mental health dimension compared to general health effects, and (iii) the integration of climate and mental health data. Using data from 1970 to 2020, we found a strong correlation between climate change and rising mental disorders globally. Regional patterns emerged, with Asia, Africa, and Oceania showing broader associations with various mental health issues, while Europe and the Americas saw increases in anxiety and depression. This study contributes to a more comprehensive understanding of the interconnectedness between climate change, mental health, and sustainability. By addressing the mental health impacts of climate change, we can identify sustainable solutions that promote both environmental well-being and human well-being. Our findings highlight the urgent need for global action to mitigate climate change’s mental health effects and provide insights for tailored interventions and public health strategies. Additionally, socio-economic factors like unemployment, urbanisation, GDP growth, and globalisation are incorporated to explore the intricate interplay between climate change, mental health, and societal contexts, offering a clearer understanding of the mechanisms at play.

The rapid speed of urbanization in modern cities has led to various environmental challenges impacting human activities, livelihoods, and comfort. One of these effects is the urban heat island, which describes the increase in temperature in an urban area resulting from the replacement of natural surfaces with concrete, buildings, and other structures that absorb and retain heat. Variations in individual perception and adaptative ability present additional challenges when trying to ensure outdoor comfort and require advanced measuring instruments and simulation tools to accurately predict a broad range of related variables. In this study, we investigated three different types of blue–green spaces (six in total) on the campus of Guangzhou University, focusing on their distinct layouts. The aim was to evaluate these spaces’ microclimate and sunlight intensity conditions by quantifying several environmental factors. Subjective comfort assessments and objective physiological parameter measurements were conducted using questionnaires and biosensors, respectively. The results revealed the following: (1) Different types of blue–green spaces exhibit distinct microclimate and visual environment characteristics, and while similar patterns emerged, certain environmental parameters revealed important differences. (2) There is a significant association between individuals’ thermal and visual comfort in blue–green spaces and multiple environmental factors. (3) Linear regression analysis demonstrated the strong predictive capabilities of skin conductance indicators (Rsc, SCR, and nSCR) in assessing individuals’ outdoor visual–thermal comfort levels, with R2 exceeding 0.5, indicating high accuracy. These findings provide valuable insights and references for urban planners and designers seeking to enhance the visual and thermal aspects of sustainable landscapes on campuses as well as in other outdoor environments.

Enterprise financial risk prediction is a fundamental task in financial risk management, yet its performance is often hindered by severe class imbalance, cross-enterprise heterogeneity, and the limited availability of labeled risky samples. These challenges are particularly pronounced in few-shot settings, where conventional machine learning and deep classification models tend to suffer from unstable representation learning, feature collapse, and weak decision boundaries. To address this issue, this study proposes a hierarchical metric learning framework for few-shot enterprise financial risk prediction on imbalanced tabular data. The framework integrates a state-space feature embedding network, an Adaptive Spectral Decomposition and Multi-Scale State Embedding module, and a Hierarchical Metric Manifold Alignment mechanism to enhance risk-sensitive representation learning, preserve geometric consistency across embedding levels, and improve prototype-based discrimination in the metric space. Experiments are conducted on three public datasets, namely American Bankruptcy, Corporate Financial Risk Assessment, and Enterprise Financial Network, under a unified 2-way 20-shot setting. The proposed method consistently achieves the best overall performance across Precision, Recall, Accuracy, F1-score, and AUC, with AUC values of 0.9526, 0.9687, and 0.9716 on the three datasets, respectively. Ablation studies and visual analyses further show that the proposed framework improves intra-class compactness, inter-class separability, and classification robustness under highly imbalanced conditions. These findings indicate that the proposed method provides an effective and robust machine learning solution for enterprise financial risk prediction and early warning in data-scarce financial scenarios.